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Genetic interaction networks From C. elegans to human disease

Most hereditary diseases in humans are genetically complex, resulting from combinations of mutations in multiple genes. However synthetic interactions between genes are very difficult to identify in population studies because of a... ver más

30/04/2014

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FUNDACIO PRIVADA CENTRE DE REGULACIO GENOMICA Otras actividades deportivas asociacion

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FUNDACIO PRIVADA CENTRE DE REGULACIO GENOMICA Otras actividades deportivas asociacion

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Presupuesto del proyecto 1M€

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Descripción del proyecto Most hereditary diseases in humans are genetically complex, resulting from combinations of mutations in multiple genes. However synthetic interactions between genes are very difficult to identify in population studies because of a lack of statistical power and we fundamentally do not understand how mutations interact to produce phenotypes. C. elegans is a unique animal in which genetic interactions can be rapidly identified in vivo using RNA interference, and we recently used this system to construct the first genetic interaction network for any animal, focused on signal transduction genes. The first objective of this proposal is to extend this work and map a comprehensive genetic interaction network for this model metazoan. This project will provide the first insights into the global properties of animal genetic interaction networks, and a comprehensive view of the functional relationships between genes in an animal. The second objective of the proposal is to use C. elegans to develop and validate experimentally integrated gene networks that connect genes to phenotypes and predict genetic interactions on a genome-wide scale. The methods that we develop and validate in C. elegans will then be applied to predict phenotypes and interactions for human genes. The final objective is to dissect the molecular mechanisms underlying genetic interactions, and to understand how these interactions evolve. The combined aim of these three objectives is to generate a framework for understanding and predicting how mutations interact to produce phenotypes, including in human disease.

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